# Rfd4 Proteina Search

Use when running RFD4-Proteina reward-guided beam search with chosen checkpoints...

- Repository: https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit (503 stars)
- Category: Engineering
- License: CC-BY-4.0
- Last updated: 2026-10-28
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add NVIDIA-BioNeMo/bionemo-agent-toolkit --skill rfd4-proteina-search -g -a claude-code -y`
Codex: `npx skills add NVIDIA-BioNeMo/bionemo-agent-toolkit --skill rfd4-proteina-search -g -a codex -y`
Cursor: `npx skills add NVIDIA-BioNeMo/bionemo-agent-toolkit --skill rfd4-proteina-search -g -a cursor -y`
Gemini CLI: `npx skills add NVIDIA-BioNeMo/bionemo-agent-toolkit --skill rfd4-proteina-search -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add NVIDIA-BioNeMo/bionemo-agent-toolkit`

## Description

Use when running RFD4-Proteina reward-guided beam search with chosen checkpoints, branching, metrics, and compute limits.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# RFD4-Proteína Reward-Guided Search

## Purpose

## Reward requirements

Complete `rfd4-proteina-setup` first. Generation requires a CUDA GPU. Rewards
listing refolding oracles also require a configured refolding backend; RFD4-Proteína
supports AF2, AF3, and OpenFold3 through `metrics/refolding_oracles`. Rewards such
as `interchain_interactions` do not require a refolding oracle.

Search explores multiple generation trajectories and keeps the candidates a
**reward** scores highest. **Beam search** ships ready to run: it branches and
prunes partial trajectories at step checkpoints, scoring survivors with a reward
model. The shipped `inference_beam_example` uses the default **AF3 oracle** for
refolding confidence; other search configs may select another backend or avoid
refolding entirely.

Search runs through the **same entry point as generation**, `generate.py`, selected by an `inference_experiment` config that adds one `search:` block. All
arguments are Hydra `key=value` (never `--key value`).

Run everything from the repository root (the directory containing `.project-root`).

## Prerequisites

A working generation runtime, validated design inputs, and the dependencies of each selected reward provider.

Use the user-selected model checkout at `RFPROTEINA_ROOT` for model commands,
configs, and upstream manuals. This catalog contains the skills, not that checkout.
In helper examples, resolve `open-models-skills/rfd4-proteina/<name>/...` to its absolute installed path
when the catalog and model checkout are separate.

## Inputs

Required: the user's inputs, output directory, reward objective, and compute budget. Optional: checkpoint, beam, branch, and reward-weight overrides. Read other defaults from the chosen config and preserve selected scheduler resources.

## Instructions

### Step 1, Preflight the selected reward dependencies

Every search needs a green rfd4-proteina-setup:

The rest: https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit/blob/HEAD/open-models-skills/rfd4-proteina/rfd4-proteina-search/SKILL.md